Journal of Guangxi Teachers Education University (Philosophy and Social Sciences Edition) ›› 2025, Vol. 61 ›› Issue (5): 69-78.doi: 10.16088/j.issn.1001-6597.2025.05.008

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Definition of Subjects and Implementation Pathways for Generative AI Labeling Obligations

ZHENG Zhi-feng, CHEN Jing   

  1. School of Civil and Commercial Law, Southwest University of Political Science and Law, Chongqing 401120, China
  • Received:2025-02-17 Online:2025-09-05 Published:2025-09-18

Abstract: The labeling obligations for generative artificial intelligence involve tripartite responsibilities among users, service providers, and technical supporters. Users, as primary content generators, bear the fundamental obligation of basic labeling; service providers, serving as core actors, must ensure the accuracy and completeness of labels; technical supporters should employ technological measures to guarantee effective implementation of labeling mechanisms. In terms of implementation pathways, labeling obligations should be tailored based on content types and application scenarios: A multi-tiered and differentiated labeling framework should be established for purely AI-generated content, human-AI collaborative content, and suspected AI-generated content; Stringent labeling requirements, guided by risk stratification principles, should apply to high-risk generated content, simplified mechanisms for low-risk content, and customized standards for industry-specific generated content. This approach facilitates the high-quality and sustainable development of generative AI technology within a compliance framework.

Key words: generative artificial intelligence, risk stratification, labeling standards, obligation fulfillment

CLC Number:  G203
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